Fix per file ruff ignores related to simplification (#26259)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/kernels/attention/test_cutlass_mla_decode.py

@@ -66,10 +66,7 @@ def test_cutlass_mla_decode(
     b, s_q, mean_sk, h_q, h_kv, d, dv, block_size, causal, varlen, torch_dtype
 ):
     device = torch.device("cuda:0")
-    if torch_dtype == torch.float8_e4m3fn:
-        init_dtype = torch.bfloat16
-    else:
-        init_dtype = torch_dtype
+    init_dtype = torch.bfloat16 if torch_dtype == torch.float8_e4m3fn else torch_dtype
     torch.set_default_dtype(init_dtype)
     torch.set_default_device(device)
     torch.cuda.set_device(device)

tests/kernels/attention/test_flashmla.py

@@ -52,10 +52,7 @@ def test_flash_mla(
     b, s_q, mean_sk, h_q, h_kv, d, dv, block_size, causal, varlen, torch_dtype
 ):
     device = torch.device("cuda:0")
-    if torch_dtype == torch.float8_e4m3fn:
-        init_dtype = torch.bfloat16
-    else:
-        init_dtype = torch_dtype
+    init_dtype = torch.bfloat16 if torch_dtype == torch.float8_e4m3fn else torch_dtype
     torch.set_default_dtype(init_dtype)
     torch.set_default_device(device)
     torch.cuda.set_device(device)

tests/kernels/attention/test_lightning_attn.py

@@ -33,10 +33,7 @@ def reference_lightning_attention(q, k, v, ed, block_size, kv_history):
 
     # More efficient implementation
     # Convert decay factors to matrix form
-    if ed.dim() == 1:
-        decay = torch.exp(-ed).view(1, -1, 1, 1)
-    else:
-        decay = torch.exp(-ed)
+    decay = torch.exp(-ed).view(1, -1, 1, 1) if ed.dim() == 1 else torch.exp(-ed)
 
     for b in range(B):
         for step in range(S):

tests/kernels/moe/test_pplx_moe.py

@@ -705,10 +705,7 @@ def _pplx_moe(
         with set_current_vllm_config(vllm_config), override_config(moe_config):
             topk_weight, topk_ids, _ = fused_topk(a, score, topk, False)
 
-            if shared_experts is not None:
-                shared_output = shared_experts(a)
-            else:
-                shared_output = None
+            shared_output = shared_experts(a) if shared_experts is not None else None
 
             torch_output = torch_experts(
                 a,

tests/kernels/quantization/test_cutlass_scaled_mm.py

@@ -88,10 +88,7 @@ def cutlass_fp8_gemm_helper(
     # make scales K-major for blockwise quant, doesn't affect 1D scales
     scale_b = scale_b.t().contiguous().t()
 
-    if use_bias:
-        bias = torch.rand((n,), device=device, dtype=out_dtype) * 10
-    else:
-        bias = None
+    bias = torch.rand((n,), device=device, dtype=out_dtype) * 10 if use_bias else None
 
     out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
@@ -122,10 +119,7 @@ def cutlass_int8_gemm_helper(
     scale_a = torch.randn(a_scales_shape, device=device, dtype=torch.float32)
     scale_b = torch.randn(b_scales_shape, device=device, dtype=torch.float32)
 
-    if use_bias:
-        bias = torch.rand((n,), device=device, dtype=out_dtype) * 10
-    else:
-        bias = None
+    bias = torch.rand((n,), device=device, dtype=out_dtype) * 10 if use_bias else None
 
     out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)

tests/kernels/test_onednn.py

@@ -84,10 +84,7 @@ def onednn_int8_gemm_test_helper(
         azp = None
         azp_adj = None
 
-    if use_bias:
-        bias = torch.rand((n,), device=device, dtype=out_dtype) * 10
-    else:
-        bias = None
+    bias = torch.rand((n,), device=device, dtype=out_dtype) * 10 if use_bias else None
 
     handler = ops.create_onednn_scaled_mm(
         b,

tests/kernels/utils.py

@@ -963,13 +963,9 @@ def make_test_metadata(
             None if encoder_seq_lens is None else (sum(encoder_seq_lens))
         )
 
-    if cross_test_params is None:
-        cross_kv_mmap = None
-    else:
-        # Encoder/decoder or encoder-only models only:
-        # * Extract *cross-attention* slot_mapping and block table
-        #   (kv_mmap)
-        cross_kv_mmap = cross_test_params.kv_mmap
+    # For encoder/decoder or encoder-only models only, extract *cross-attention*
+    # slot_mapping and block table (kv_mmap)
+    cross_kv_mmap = None if cross_test_params is None else cross_test_params.kv_mmap
 
     attn_backend_obj = make_backend(attn_backend.name)